visualise random vs sequential bit allocations

bits.py

import random
import numpy as np
from matplotlib import pyplot as plt


fixed_bits    = 32
variable_bits = 32
bitlen        = fixed_bits + variable_bits
iterations    = 2048


def longest_match(old, new):
	old = bin(old)[2:]
	new = bin(new)[2:]

	for i in range(0, len(old)):
		if old[i] != new[i]:
			return i+1
	return -1

def plot(counts, name, subheading):
	a = np.array(counts)

	fig = plt.figure(figsize=[7,5])
	ax = plt.subplot(111)

	plt.xticks(np.arange(0, bitlen+1, step=8))
	ax.set_xlabel('longest bitmatch')
	ax.set_ylabel('frequency')
	ax.set_title(name + ": " + subheading)

	ax.bar(range(0,len(counts)),counts)
	plt.savefig(name + ".png")



# Set a string of 1s
network = (2**bitlen)-1
print("1 ",network,bin(network))
# Squash the variable part to zeroes
# Acts like a netmask
network = network - ((2 ** variable_bits)-1)
print("2 ",network,bin(network))


# count into these bins
# use for histogram later
histo_counts = [0] * bitlen

old_ip  = network
for i in range(1, iterations):
	r = random.randrange(0,2**variable_bits)
	new_ip = network | r

	n = longest_match(old_ip, new_ip)
	print(n, hex(old_ip), hex(new_ip))
	histo_counts[n-1] += 1

	old_ip = new_ip

plot(histo_counts, "random", str(fixed_bits) + " fixed + " + str(variable_bits) + " bits variable, " + str(iterations) + " iterations")

# reset counts
histo_counts = [0] * bitlen
old_ip = network
for i in range(1, iterations):
	new_ip = old_ip + 1
	n = longest_match(old_ip, new_ip)
	print(n, hex(old_ip), hex(new_ip))

	histo_counts[n-1] += 1

	old_ip = new_ip

plot(histo_counts, "linear", str(fixed_bits) + "fixed  + " + str(variable_bits) + " bits variable, " + str(iterations) + " iterations")

linear.png

random.png